Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C.L., Lindberg, E.L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020). Cells of the adult human heart. Nature 588, 466–472.

BioProject Accession: PRJEB39602
GEO Accession: ERP123138

Load required packages.

library(tidyverse)
library(magrittr)
library(Matrix)
library(patchwork)
library(extrafont)
library(reticulate)

Sys.time()

## [1] "2021-02-03 20:52:16 CST"

Data preparation

Functions

source(
    file = file.path(
        SCRIPT_DIR,
        "utilities.R"
    )
)

PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/HCM/"

Anndata

Load re-mapped UMI count matrix stored in the h5ad file.

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)

## [1] "0.7.5"

adata <- ad$read_h5ad(
    filename = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602/matrices/predefined",
        "adata.h5ad"
    ),
    backed = "r"
)

Metadata

Raw bam files were downloaded at https://www.ebi.ac.uk/ena/browser/view/PRJEB39602.

file_info <- readr::read_delim(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602",
        "filereport_read_run_PRJEB39602_tsv.txt"
    ),
    delim = "\t"
)

## 
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   study_accession = col_character(),
##   sample_accession = col_character(),
##   experiment_accession = col_character(),
##   run_accession = col_character(),
##   tax_id = col_double(),
##   scientific_name = col_character(),
##   fastq_md5 = col_logical(),
##   fastq_ftp = col_logical(),
##   submitted_md5 = col_character(),
##   submitted_ftp = col_character(),
##   sra_md5 = col_logical(),
##   sra_ftp = col_logical()
## )

cell_metadata_PRJEB39602 <- read_csv(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602",
        "SraRunTable.txt"
    )
)

## 
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_character(),
##   `ENA-FIRST-PUBLIC (run)` = col_date(format = ""),
##   `ENA-FIRST-PUBLIC` = col_date(format = ""),
##   `ENA-LAST-UPDATE (run)` = col_date(format = ""),
##   `ENA-LAST-UPDATE` = col_date(format = ""),
##   `nominal_length (exp)` = col_double(),
##   `nominal_sdev (exp)` = col_double(),
##   ReleaseDate = col_datetime(format = "")
## )
## ℹ Use `spec()` for the full column specifications.

file_info %>%
    select(sample_accession, experiment_accession, submitted_ftp) %>%
    mutate(
        base_name = str_remove(submitted_ftp, ";.+$"),
        base_name = str_remove(base_name, "^.+/"),
        base_name = str_remove(base_name, "_.+$") %>% str_remove(".bam")
    ) %>%
    select(-submitted_ftp) %>%
    unique() %>%
    left_join(
        cell_metadata_PRJEB39602 %>%
            select(
                sample_accession = BioSample, library_name = `Library Name`
            ) %>%
            unique(),
        by = "sample_accession"
    )

Formatting

Load metadata released at the HCA portal.

# download from hca
cell_metadata_hca <- readr::read_delim(
    file = file.path(
        PROJECT_DIR,
        "docs/Cells_of_the_adult_human_heart",
        "HeartSingleCellsAndNucleiSeq_2020-12-21_03.45.tsv"
    ),
    delim = "\t"
)

## 
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_character(),
##   bundle_version = col_datetime(format = ""),
##   cell_suspension.biomaterial_core.ncbi_taxon_id = col_double(),
##   cell_suspension.estimated_cell_count = col_double(),
##   cell_suspension.provenance.schema_major_version = col_double(),
##   cell_suspension.provenance.schema_minor_version = col_double(),
##   collection_protocol.provenance.schema_major_version = col_double(),
##   collection_protocol.provenance.schema_minor_version = col_double(),
##   dissociation_protocol.provenance.schema_major_version = col_double(),
##   dissociation_protocol.provenance.schema_minor_version = col_double(),
##   donor_organism.biomaterial_core.ncbi_taxon_id = col_double(),
##   donor_organism.provenance.schema_major_version = col_double(),
##   donor_organism.provenance.schema_minor_version = col_double(),
##   enrichment_protocol.maximum_size = col_double(),
##   enrichment_protocol.provenance.schema_major_version = col_double(),
##   enrichment_protocol.provenance.schema_minor_version = col_double(),
##   file_format = col_logical(),
##   file_size = col_double(),
##   file_version = col_datetime(format = ""),
##   library_preparation_protocol.cell_barcode.barcode_length = col_double(),
##   library_preparation_protocol.cell_barcode.barcode_offset = col_double()
##   # ... with 17 more columns
## )
## ℹ Use `spec()` for the full column specifications.

cell_metadata_hca %>%
    select(
        sample_accession = `specimen_from_organism.biomaterial_core.biosamples_accession`,
        base_name = file_name,
        sample_description = `specimen_from_organism.organ_parts.text`,
        sample_biomaterial_id = `specimen_from_organism.biomaterial_core.biomaterial_id`,
        cell = `cell_suspension.biomaterial_core.biomaterial_description`
    ) %>%
    mutate(
        base_name = str_remove(base_name, "_S[0-9]{1}_.+$"),
        cell = str_remove(cell, " .+$")
    ) %>%
    unique()

Load embedding released along with the publication (extracted from h5ad file hosted at the HCA portal).

embedding_predefined <- read_csv(
    file = file.path(
        PROJECT_DIR,
        "raw/public/PRJEB39602/ad98d3cd-26fb-4ee3-99c9-8a2ab085e737.hca_heart_global_ctl200723_freeze",
        "embedding_global_raw.gz"
    )
)

## Warning: Missing column names filled in: 'X1' [1]

## 
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   .default = col_character(),
##   n_counts = col_double(),
##   n_genes = col_double(),
##   percent_mito = col_double(),
##   percent_ribo = col_double(),
##   scrublet_score = col_double(),
##   x_umap = col_double(),
##   y_umap = col_double(),
##   x_pca = col_double(),
##   y_pca = col_double()
## )
## ℹ Use `spec()` for the full column specifications.

ID, matching downloaded files

Match downloaded bam files to the ids used in the h5ad file provided on the HCA portal.

cell_ids <- tibble::tribble(
    ~base_name, ~predefined_id,
    "5f1b169afe9c934c8b836260", "H0037_Apex",
    "5f1b169afe9c934c8b83625f", "H0025_RV",
    "5f1b169afe9c934c8b83625e", "H0020_septum",
    "5f1b169afe9c934c8b83625d", "H0035_LV",
    "5f1b169afe9c934c8b83625c", "H0026_RA",
    "5f1b169afe9c934c8b83625b", "H0015_LV",
    "5f1b169afe9c934c8b83625a", "H0035_LA",
    "5f1b169afe9c934c8b836259", "H0037_RA_corr",
    "5f1b169afe9c934c8b836258", "H0026_RV",
    "5f1b169afe9c934c8b836257", "H0037_RV",
    "5f1b169afe9c934c8b836256", "H0037_septum",
    "5f1b169afe9c934c8b836255", "H0037_LA_corr",
    "5f1b169afe9c934c8b836254", "H0015_RV",
    "5f1b1699fe9c934c8b836253", "H0020_RA",
    "5f1b1699fe9c934c8b836252", "H0035_septum",
    "5f1b168ffe9c934c8b8361f5", "HCAHeart7836682",
    "5f1b1691fe9c934c8b8361fc_1", "HCAHeart7656539",
    "5f1b1691fe9c934c8b8361fc_2", "HCAHeart7702880",
    "5f1b1696fe9c934c8b83622c", "HCAHeart8287125",
    "5f1b1694fe9c934c8b836222", "HCAHeart8287124",
    "5f1b1698fe9c934c8b836241", "H0015_RA",
    "5f1b1699fe9c934c8b836251", "H0026_LA",
    "5f1b1698fe9c934c8b836245", "H0020_LA_new",
    "5f1b1694fe9c934c8b83621a", "HCAHeart7880863",
    "5f1b1698fe9c934c8b836240", "H0035_RA",
    "5f1b1697fe9c934c8b836237", "H0026_septum2",
    "5f1b1693fe9c934c8b836213", "HCAHeart7833853",
    "5f1b1698fe9c934c8b83623e", "HCAHeart8287128",
    "5f1b1694fe9c934c8b836221", "HCAHeart8287123",
    "5f1b1699fe9c934c8b836248", "H0020_LV",
    "5f1b1697fe9c934c8b836234", "H0020_apex",
    "5f1b1695fe9c934c8b836227", "HCAHeart7888926",
    "5f1b1692fe9c934c8b83620a", "HCAHeart7888922",
    "5f1b168ffe9c934c8b8361e8_1", "HCAHeart7664654",
    "5f1b168ffe9c934c8b8361e8_2", "HCAHeart7757636",
    "5f1b168ffe9c934c8b8361e8_3", "HCAHeart7985086",
    "5f1b168ffe9c934c8b8361ea", "HCAHeart7833852",
    "5f1b1699fe9c934c8b83624e", "H0025_apex",
    "5f1b1697fe9c934c8b83623c", "H0035_RV",
    "5f1b168ffe9c934c8b8361e5_1", "HCAHeart7829976",
    "5f1b168ffe9c934c8b8361e5_2", "HCAHeart7985089",
    "5f1b168ffe9c934c8b8361e7", "HCAHeart7836683",
    "5f1b1695fe9c934c8b83622a", "H0015_septum",
    "5f1b1699fe9c934c8b83624f", "H0025_LA",
    "5f1b168efe9c934c8b8361db_1", "HCAHeart7702881",
    "5f1b168efe9c934c8b8361db_2", "HCAHeart7702882",
    "5f1b1694fe9c934c8b836220", "HCAHeart7833854",
    "5f1b168ffe9c934c8b8361f3", "HCAHeart7757639",
    "5f1b168efe9c934c8b8361e0", "HCAHeart7964513",
    "5f1b168efe9c934c8b8361e2_1", "HCAHeart7664653",
    "5f1b168efe9c934c8b8361e2_2", "HCAHeart7702873",
    "5f1b1694fe9c934c8b836218", "HCAHeart7880861",
    "5f1b1699fe9c934c8b83624b", "HCAHeart8287126",
    "5f1b1698fe9c934c8b836243", "H0037_LV",
    "5f1b1692fe9c934c8b836207", "HCAHeart7829979",
    "5f1b168ffe9c934c8b8361e4", "HCAHeart7698016",
    "5f1b1699fe9c934c8b83624c", "H0035_apex",
    "5f1b168efe9c934c8b8361e1_1", "HCAHeart7757638",
    "5f1b168efe9c934c8b8361e1_2", "HCAHeart7985088",
    "5f1b1697fe9c934c8b83623a", "H0015_LA_new",
    "5f1b168ffe9c934c8b8361eb_1", "HCAHeart7702874",
    "5f1b168ffe9c934c8b8361eb_2", "HCAHeart7702875",
    "5f1b168ffe9c934c8b8361f1", "HCAHeart7835149",
    "5f1b168efe9c934c8b8361dd", "HCAHeart7698017",
    "5f1b1691fe9c934c8b8361f8", "HCAHeart7888927",
    "5f1b168efe9c934c8b8361d9", "HCAHeart7829977",
    "5f1b1696fe9c934c8b836232", "H0025_LV",
    "5f1b1691fe9c934c8b836203", "HCAHeart7888929",
    "5f1b1690fe9c934c8b8361f6", "HCAHeart7836681",
    "5f1b1695fe9c934c8b836226", "HCAHeart7888925",
    "5f1b1699fe9c934c8b83624a", "H0026_LV_V3",
    "5f1b168ffe9c934c8b8361f0_1", "HCAHeart7664652",
    "5f1b168ffe9c934c8b8361f0_2", "HCAHeart7698015",
    "5f1b1691fe9c934c8b8361fe", "HCAHeart7880860",
    "5f1b1699fe9c934c8b83624d", "H0015_apex",
    "5f1b168ffe9c934c8b8361ed_1", "HCAHeart7702878",
    "5f1b168ffe9c934c8b8361ed_2", "HCAHeart7702879",
    "5f1b168ffe9c934c8b8361e3", "HCAHeart7833855",
    "5f1b168ffe9c934c8b8361ef_1", "HCAHeart7702876",
    "5f1b168ffe9c934c8b8361ef_2", "HCAHeart7702877",
    "5f1b168ffe9c934c8b8361f2", "HCAHeart7835148",
    "5f1b1692fe9c934c8b836210", "HCAHeart7888924",
    "5f1b168efe9c934c8b8361de_1", "HCAHeart7757637",
    "5f1b168efe9c934c8b8361de_2", "HCAHeart7985087",
    "5f1b1696fe9c934c8b83622e", "H0026_apex",
    "5f1b1693fe9c934c8b836217", "HCAHeart7888923",
    "5f1b1697fe9c934c8b83623d", "HCAHeart7888928",
    "5f1b1697fe9c934c8b836235", "H0020_RV",
    "5f1b1698fe9c934c8b83623f", "H0025_RA",
    "5f1b1697fe9c934c8b836236", "HCAHeart7836684",
    "5f1b1698fe9c934c8b836242", "H0025_septum",
    "5f1b1691fe9c934c8b8361fb", "HCAHeart7880862",
    "5f1b1698fe9c934c8b836247", "HCAHeart8287127",
    "5f1b1692fe9c934c8b836209", "HCAHeart7829978",
    "5f1b1695fe9c934c8b836229", "HCAHeart8102865",
    "5f1b1695fe9c934c8b836228", "HCAHeart7850548",
    "5f1b1692fe9c934c8b83620c", "HCAHeart7844004",
    "5f1b1694fe9c934c8b83621f", "HCAHeart7905331",
    "5f1b1694fe9c934c8b83621b", "HCAHeart7728605",
    "5f1b1695fe9c934c8b836223", "HCAHeart8102866",
    "5f1b1694fe9c934c8b836219", "HCAHeart7850545",
    "5f1b1696fe9c934c8b83622f", "HCAHeart7844000",
    "5f1b1692fe9c934c8b836211", "HCAHeart7843999",
    "5f1b1694fe9c934c8b83621e", "HCAHeart7844002",
    "5f1b1692fe9c934c8b83620e", "HCAHeart7850539",
    "5f1b1692fe9c934c8b83620b", "HCAHeart7728607",
    "5f1b168ffe9c934c8b8361e6", "HCAHeart7656538",
    "5f1b1691fe9c934c8b836202", "HCAHeart7905329",
    "5f1b1691fe9c934c8b8361ff", "HCAHeart8102868",
    "5f1b1691fe9c934c8b8361fd", "HCAHeart7745969",
    "5f1b1696fe9c934c8b83622d", "HCAHeart8102861",
    "5f1b168efe9c934c8b8361df", "HCAHeart7728609",
    "5f1b1691fe9c934c8b836205", "HCAHeart7905327",
    "5f1b168efe9c934c8b8361da", "HCAHeart7656534",
    "5f1b1691fe9c934c8b836204", "HCAHeart7745966",
    "5f1b168ffe9c934c8b8361ec", "HCAHeart7606896",
    "5f1b1695fe9c934c8b836225", "HCAHeart7850541",
    "5f1b1693fe9c934c8b836214", "HCAHeart8102863",
    "5f1b1693fe9c934c8b836216", "HCAHeart7850542",
    "5f1b1691fe9c934c8b836201", "HCAHeart7844001",
    "5f1b1694fe9c934c8b83621d", "HCAHeart7850543",
    "5f1b1696fe9c934c8b836231", "HCAHeart7905330",
    "5f1b1697fe9c934c8b83623b", "HCAHeart7850547",
    "5f1b1691fe9c934c8b8361f7", "HCAHeart7728604",
    "5f1b168ffe9c934c8b8361f4", "HCAHeart7728606",
    "5f1b1691fe9c934c8b836200", "HCAHeart7745970",
    "5f1b168ffe9c934c8b8361e9", "HCAHeart7656536",
    "5f1b1692fe9c934c8b836208", "HCAHeart7850549",
    "5f1b168ffe9c934c8b8361ee", "HCAHeart7656537",
    "5f1b1695fe9c934c8b836224", "HCAHeart7905328",
    "5f1b1694fe9c934c8b83621c", "HCAHeart7850540",
    "5f1b1691fe9c934c8b8361f9", "HCAHeart7905332",
    "5f1b168efe9c934c8b8361dc", "HCAHeart7656535",
    "5f1b1691fe9c934c8b8361fa", "HCAHeart7745967",
    "5f1b1692fe9c934c8b836206", "HCAHeart7728608",
    "5f1b1695fe9c934c8b83622b", "HCAHeart7850544",
    "5f1b1696fe9c934c8b836230", "HCAHeart8102864",
    "5f1b1698fe9c934c8b836244", "HCAHeart8102867",
    "5f1b1696fe9c934c8b836233", "HCAHeart8102862",
    "5f1b1692fe9c934c8b83620d", "HCAHeart7751845",
    "5f1b1693fe9c934c8b836215", "HCAHeart8102858",
    "5f1b1697fe9c934c8b836238", "HCAHeart7844003",
    "5f1b1697fe9c934c8b836239", "HCAHeart8102860",
    "5f1b1692fe9c934c8b83620f", "HCAHeart7745968",
    "5f1b1699fe9c934c8b836249", "HCAHeart7850551",
    "5f1b1698fe9c934c8b836246", "HCAHeart8102857",
    "5f1b1692fe9c934c8b836212", "HCAHeart7850546",
    "5f1b1699fe9c934c8b836250", "HCAHeart8102859"
)

Cross-reference single cell/nucleus identity and metadata (EBI, HCA metadata, HCA h5ad).

cells_predefined <- embedding_predefined %>%
    dplyr::select(X1, sample) %>%
    dplyr::left_join(cell_ids, by = c("sample" = "predefined_id")) %>%
    dplyr::mutate(
        cell = str_c(base_name, str_extract(X1, "[A-Z]{16}"), sep = "_")
    ) %>%
    dplyr::left_join(
        adata$obs %>%
            as.data.frame() %>%
            tibble::rownames_to_column(var = "cell")
    )

## Joining, by = "cell"

embedding_predefined <- embedding_predefined %>%
    left_join(cells_predefined)

## Joining, by = c("X1", "sample")

embedding_predefined %>%
    dplyr::select(
        NRP, age_group, cell_source, donor, gender,
        region, sample, source, type, version
    ) %>%
    unique()

Sample: SAMEA7249652 (CD45+ cells from specimen CBTM-473C_RA) not included in the publication. https://www.ebi.ac.uk/ena/browser/view/ERS5009270

Biomaterial Id: CBTM-473C_RA_cd45_pos

HCA_Biomaterial_UUID: 2e030e2a-3df4-4eec-8513-8833819bece5

Library name: CBTM-473C_RA_cd45_pos

Summary

Cell types, based on the publication

embedding_predefined %>%
    dplyr::group_by(cell_type) %>%
    dplyr::summarize(
        num_cell = n(),
        median_umi_reported = median(n_counts),
        median_features_reported = median(n_genes),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

cell_type	num_cell	median_umi_reported	median_features_reported	median_umi	median_features
Adipocytes	3799	3875.0	1707.0	3764.0	1682.0
Atrial_Cardiomyocyte	23483	2868.0	1399.0	2792.0	1358.0
doublets	623	688.0	484.0	709.0	493.0
Endothelial	100579	1585.0	909.0	1690.0	959.0
Fibroblast	59341	1195.0	757.0	1196.0	758.0
Lymphoid	17217	1552.0	831.0	1560.0	845.0
Mesothelial	718	1967.0	1082.5	2022.5	1082.0
Myeloid	23028	1655.5	896.0	1665.5	913.0
Neuronal	3961	1039.0	716.0	997.0	693.0
NotAssigned	33998	2186.0	1230.0	2215.0	1250.5
Pericytes	77856	1321.0	790.0	1337.0	806.0
Smooth_muscle_cells	16242	1831.0	988.0	1790.0	988.0
Ventricular_Cardiomyocyte	125289	3621.0	1545.0	3706.0	1588.0

Anatomical region

embedding_predefined %>%
    dplyr::group_by(region) %>%
    dplyr::summarize(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

region	num_cell	median_umi	median_features
AX	98251	1973	1047
LA	72734	1771	978
LV	107261	2256	1166
RA	41503	1440	868
RV	84519	2308	1186
SP	81866	2015	1058

Chromium Single Cell 3’ Reagent Kit version

embedding_predefined %>%
    group_by(version) %>%
    summarise(
        num_cell = n(),
        median_umi_reported = median(n_counts),
        median_features_reported = median(n_genes),
        median_umi = median(num_umis),
        median_features = median(num_features)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

version	num_cell	median_umi_reported	median_features_reported	median_umi	median_features
V2	238154	1511	837	1536	850
V3	247980	2367	1253	2468	1310

Cell source

embedding_predefined %>%
    group_by(cell_source) %>%
    summarise(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_source	num_cell	median_umi	median_features
Harvard-Nuclei	178161	2201	1236
Sanger-CD45	80597	2079	1004
Sanger-Cells	45885	3468	1590
Sanger-Nuclei	181491	1442	845

Source

embedding_predefined %>%
    group_by(source) %>%
    summarise(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

source	num_cell	median_umi	median_features
CD45+	80597	2079	1004
Cells	45885	3468	1590
Nuclei	359652	1830	1034

Age group

embedding_predefined %>%
    group_by(age_group) %>%
    summarise(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

age_group	num_cell	median_umi	median_features
40-45	26934	3050	1544
45-50	32336	2192	1232
50-55	108472	2163	1196
55-60	52838	1437	863
60-65	128207	2136	1079
65-70	108449	1930	983
70-75	28898	1162	702

Donor

embedding_predefined %>%
    group_by(donor) %>%
    summarise(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

donor	num_cell	median_umi	median_features
D1	15397	1997.0	895
D11	48930	2909.5	1325
D2	43143	1414.0	845
D3	27022	1217.0	757
D4	28898	1162.0	702
D5	28799	1416.0	759
D6	79650	2097.0	1068
D7	36134	2182.5	1074
H2	22216	1841.0	1107
H3	32458	2118.0	1227
H4	25816	1648.0	974
H5	38401	2482.0	1337
H6	26934	3050.0	1544
H7	32336	2192.0	1232

Type: Donation after Circulatory Death (DCD), Donation after Brain Death (DCD)

embedding_predefined %>%
    group_by(type) %>%
    summarise(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

type	num_cell	median_umi	median_features
DBD	220580	2076	1157
DCD	265554	1943	1000

Adult human heart’s celluar landscape

Pre-defined

Cell embedding and annotation are extracted from h5ad file provided on the HCA portal.

EMBEDDING_TITLE_PREFIX <- "UMAP"
embedding_type <- "predefined"

Cell type & region

GEOM_POINT_SIZE <- 0.1
p_embedding_predefined_cell_type <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$cell_type %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Cell type"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = FALSE
) +
    theme_customized()

p_embedding_predefined_region <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$region %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Region"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_ncol = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$region %>% unique() %>% length()
        )
    ) +
    theme_customized()

p_embedding_predefined_donor <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$donor %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Donor"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    legend_ncol = 2
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$donor %>% unique() %>% length()
        )
    ) +
    theme_customized()

p_embedding_predefined_version <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$version %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Version"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$version %>% unique() %>% length()
        )
    ) +
    theme_customized()

p_embedding_predefined_source <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$source %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Source"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$source %>% unique() %>% length()
        )
    ) +
    theme_customized()

p_embedding_predefined_type <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$type %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Type"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = scales::hue_pal()(
            embedding_predefined$type %>% unique() %>% length()
        )
    ) +
    theme_customized()

list(
    p_embedding_predefined_cell_type,
    p_embedding_predefined_region,
    p_embedding_predefined_donor,
    p_embedding_predefined_version,
    p_embedding_predefined_source,
    p_embedding_predefined_type
) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Mitochondria ratio distribution

embedding_predefined$percent_mito %>% range()

## [1] 0.0000000 0.1999672

p_embedding_predefined_MT <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$percent_mito,
    label = paste0(EMBEDDING_TITLE_PREFIX, "; mitochondria %"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = TRUE,
    dpi = 600,
    legend_size = 3,
    legend_ncol = 1
) +
    theme_customized(
        x = 0.035,
        y = 0.995,
        legend_key_size = 1.5,
        legend_text_size = 4,
        border_color = "#b3b3b3",
        strip_color = "#b3b3b3"
    )

MT_RATIO_THRESHOLD <- 0.15
p_embedding_predefined_MT2 <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(
        embedding_predefined$percent_mito >= MT_RATIO_THRESHOLD
    ) %>% as.factor(),
    label = paste(
        "UMAP", MT_RATIO_THRESHOLD,
        sum(embedding_predefined$percent_mito >= MT_RATIO_THRESHOLD),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = c("grey70", "salmon")
    ) +
    theme_customized()

The mitochondria ratios are extracted from h5ad file download from the HCA portal. There are 0 cells with mitochondria ratio higher than 0.2.

list(p_embedding_predefined_MT, p_embedding_predefined_MT2) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

embedding_predefined %>%
    dplyr::group_by(source) %>%
    dplyr::summarise(
        num_cells = n(),
        median_mt_ratio_reported = median(percent_mito)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

source	num_cells	median_mt_ratio_reported
CD45+	80597	0.046094750
Cells	45885	0.072591010
Nuclei	359652	0.001283697

Doublets and unassigned cells

purrr::map2(list("doublets", "NotAssigned"), c(0.15, 0.15), function(x, y) {
    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap")],
        color_values = as.numeric(embedding_predefined$cell_type == x) %>% as.factor(),
        label = paste(
            "UMAP", x, sum(embedding_predefined$cell_type == x),
            sep = "; "
        ),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = FALSE,
        geom_point_size = y,
        sort_values = TRUE,
        rasterise = TRUE
    ) +
        scale_color_manual(
            values = c("grey70", "salmon")
        ) +
        theme_customized()
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

p_embedding_predefined_scrublet <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = embedding_predefined$scrublet_score,
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Scrublet_score"),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    geom_point_alpha = 1,
    sort_values = TRUE,
    shuffle_values = FALSE,
    label_size = 2.5,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = TRUE,
    dpi = 600,
    legend_size = 3,
    legend_ncol = 1
) +
    theme_customized(
        x = 0.035,
        y = 0.995,
        legend_key_size = 1.5,
        legend_text_size = 4,
        border_color = "#b3b3b3",
        strip_color = "#b3b3b3"
    )

SCRUBLET_SCORE_THRESHOLD <- 0.3
p_embedding_predefined_scrublet2 <- plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(
        embedding_predefined$scrublet_score >= SCRUBLET_SCORE_THRESHOLD
    ) %>% as.factor(),
    label = paste(
        "UMAP", SCRUBLET_SCORE_THRESHOLD,
        sum(embedding_predefined$scrublet_score >= SCRUBLET_SCORE_THRESHOLD),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = c("grey70", "salmon")
    ) +
    theme_customized()

The scrublet scores are extracted from h5ad file download from the HCA portal. There are 0 cells with scrublet scores higher than 0.3.

list(p_embedding_predefined_scrublet, p_embedding_predefined_scrublet2) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

The distribution of scrublet scores for cells annotated as “doublets” and “NotAssigned” in the publication.

embedding_predefined %>%
    dplyr::filter(cell_type %in% c("doublets", "NotAssigned")) %>%
    dplyr::group_by(cell_type) %>%
    dplyr::summarise(
        num_cells = n(),
        median_scrublet_score_reported = median(scrublet_score),
        median_mt_ratio_reported = median(percent_mito)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_type	num_cells	median_scrublet_score_reported	median_mt_ratio_reported
doublets	623	0.1254753	0.007220217
NotAssigned	33998	0.1471215	0.011605416

Inconsistent cells

Cells with low UMI counts after re-mapping.

embedding_predefined %>%
    filter(num_umis == 0) %>%
    summarise(
        num_cells = n(),
        median_umis_reported = median(n_counts),
        median_features_reported = median(n_genes)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

num_cells	median_umis_reported	median_features_reported
4065	2681	1489

plot_embedding(
    embedding = embedding_predefined[, c("x_umap", "y_umap")],
    color_values = as.numeric(embedding_predefined$num_umis == 0) %>% as.factor(),
    label = paste(
        "UMAP", sum(embedding_predefined$num_umis == 0),
        sep = "; "
    ),
    label_position = NULL,
    show_color_value_labels = FALSE,
    show_color_legend = FALSE,
    geom_point_size = 0.3,
    sort_values = TRUE,
    rasterise = TRUE
) +
    scale_color_manual(
        values = c("grey70", "salmon")
    ) +
    theme_customized()

Cell group

cells_selected_atrial <- c(
    "aCM1",
    "aCM2",
    "aCM3",
    "aCM4",
    "aCM5"
)

cells_selected_ventricular <- c(
    "vCM1",
    "vCM2",
    "vCM3",
    "vCM4",
    "vCM5"
)

cells_selected_vascular <- c(
    "EC1_cap",
    "EC10_CMC-like",
    "EC2_cap",
    "EC3_cap",
    "EC4_immune",
    "EC5_art",
    "EC6_ven",
    "EC7_atria",
    "EC8_ln",
    "EC9_FB-like",
    "Meso",
    "PC1_vent",
    "PC2_atria",
    "PC3_str",
    "PC4_CMC-like",
    "SMC1_basic",
    "SMC2_art"
)

cells_selected_fibroblast <- c(
    "FB1",
    "FB2",
    "FB3",
    "FB4",
    "FB5",
    "FB6",
    "FB7"
)

cells_selected_immune <- c(
    "B_cells",
    "CD14+Mo",
    "CD16+Mo",
    "CD4+T_cytox",
    "CD4+T_tem",
    "CD8+T_cytox",
    "CD8+T_tem",
    "DC",
    "DOCK4+MØ1",
    "DOCK4+MØ2",
    "doublets",
    "IL17RA+Mo",
    "LYVE1+MØ1",
    "LYVE1+MØ2",
    "LYVE1+MØ3",
    "Mast",
    "MØ_AgP",
    "MØ_mod",
    "Mo_pi",
    "NK",
    "NKT",
    "NØ"
)

cells_selected_adipocyte <- c(
    "Adip1",
    "Adip2",
    "Adip3",
    "Adip4"
)

cells_selected_neuronal <- c(
    "NC1",
    "NC2",
    "NC3",
    "NC4",
    "NC5",
    "NC6"
)

cells_selected <- list(
    atrial = cells_selected_atrial,
    ventricular = cells_selected_ventricular,
    vascular = cells_selected_vascular,
    fibroblast = cells_selected_fibroblast,
    immune = cells_selected_immune,
    adipocyte = cells_selected_adipocyte,
    neuronal = cells_selected_neuronal
)

GEOM_POINT_SIZE <- 0.1

purrr::map2(names(cells_selected), c(TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE), function(x, y) {
    cells_selected_in_group <- cells_selected[[x]]

    color_palette_cell_state <- setNames(
        object = c("grey70", scales::hue_pal()(length(cells_selected_in_group))),
        nm = c("Other", cells_selected_in_group)
    )

    cell_type_labels <- get_middle_points(
        embedding = embedding_predefined[, c("x_umap", "y_umap", "cell_states")],
        x = "x_umap",
        y = "y_umap",
        group = "cell_states"
    ) %>%
        filter(cell_states %in% cells_selected_in_group)

    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap")],
        color_values = embedding_predefined %>%
            mutate(
                cell_states = case_when(
                    cell_states %in% cells_selected_in_group ~ cell_states,
                    TRUE ~ "Other"
                )
            ) %>%
            pull(cell_states) %>%
            as.factor() %>%
            forcats::fct_relevel("Other", after = Inf),
        label = paste0(EMBEDDING_TITLE_PREFIX, "; ", x),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = y,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = FALSE,
        rasterise = TRUE
    ) +
        scale_color_manual(
            values = color_palette_cell_state
        ) +
        theme_customized() +
        ggrepel::geom_text_repel(
            data = cell_type_labels,
            ggplot2::aes(
                x = .data[["x_umap"]],
                y = .data[["y_umap"]],
                label = .data[["cell_states"]],
            ),
            color = "black",
            size = 1.8,
            family = "Arial",
            #
            box.padding = 0.4,
            point.padding = 1e-06,
            min.segment.length = 0,
            arrow = ggplot2::arrow(length = unit(0.015, "npc")),
            max.overlaps = Inf,
            nudge_x = 0,
            nudge_y = 0,
            #
            segment.color = "grey35",
            # segment.size = 0.25,
            segment.size = 0.1,
            segment.alpha = 1,
            # segment.inflect = TRUE,
            seed = 20201121
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

embedding_predefined %>%
    dplyr::group_by(cell_states) %>%
    dplyr::summarize(
        num_cell = n(),
        median_umi = median(num_umis),
        median_features = median(num_features),
        median_scrublet_score_reported = median(scrublet_score),
        median_mt_ratio_reported = median(percent_mito)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

cell_states	num_cell	median_umi	median_features	median_scrublet_score_reported	median_mt_ratio_reported
aCM1	12885	2547.0	1246.0	0.09822866	0.0008253095
aCM2	4733	3061.0	1534.0	0.10299003	0.0007209805
aCM3	4096	3005.5	1344.0	0.09822866	0.0007457121
aCM4	1581	3088.0	1478.0	0.10806175	0.0008806693
aCM5	188	3362.5	1616.5	0.16473318	0.0009240758
Adip1	2274	2869.5	1367.0	0.09822866	0.0005963037
Adip2	991	8433.0	3615.0	0.18575064	0.0009694193
Adip3	290	3930.5	1742.5	0.11347518	0.0006406224
Adip4	244	1933.0	1234.0	0.15555556	0.0017569620
B_cells	1195	1873.0	926.0	0.13214990	0.0224461740
CD14+Mo	1883	3458.0	1308.0	0.09822866	0.0657966300
CD16+Mo	3278	3820.0	1522.0	0.13214990	0.0436562990
CD4+T_cytox	3113	916.0	559.0	0.09822866	0.0257104190
CD4+T_tem	1047	2613.0	947.0	0.10806175	0.0313971750
CD8+T_cytox	2956	2176.0	1085.5	0.09822866	0.0205256885
CD8+T_tem	3001	1073.0	699.0	0.08177905	0.0076117980
DC	814	2411.5	1380.0	0.11926606	0.0061114308
DOCK4+MØ1	3239	902.0	641.0	0.08952959	0.0033921304
DOCK4+MØ2	1626	1256.5	844.0	0.07482993	0.0015359029
doublets	623	709.0	493.0	0.12547529	0.0072202166
EC1_cap	27417	1375.0	813.0	0.08554572	0.0384615400
EC10_CMC-like	2608	2444.5	1219.0	0.09822866	0.0378953555
EC2_cap	13445	1583.0	897.0	0.08952959	0.0343137270
EC3_cap	16897	2072.0	1089.0	0.09822866	0.0253164560
EC4_immune	5637	1545.0	886.0	0.09375000	0.0506863780
EC5_art	20317	2196.0	1178.0	0.10299003	0.0322479640
EC6_ven	8486	2114.5	1125.5	0.10299003	0.0244897960
EC7_atria	4485	1121.0	752.0	0.13934426	0.0020979021
EC8_ln	754	1536.0	997.5	0.12547529	0.0067475975
EC9_FB-like	533	2307.0	1453.0	0.16473318	0.0035375461
FB1	26632	1109.0	700.0	0.10299003	0.0020387360
FB2	13779	1202.0	777.0	0.11926606	0.0013495276
FB3	7744	1223.0	763.5	0.13214990	0.0017438692
FB4	6146	1374.5	860.0	0.11926606	0.0024545902
FB5	2518	1296.0	821.0	0.10806175	0.0028169039
FB6	1610	2685.0	1610.0	0.17475728	0.0024872102
FB7	912	3092.0	1698.5	0.17475728	0.0035532101
IL17RA+Mo	32	4715.5	2431.0	0.10823261	0.0012512804
LYVE1+MØ1	3018	2608.0	1294.5	0.07821229	0.0127186405
LYVE1+MØ2	2088	814.0	556.0	0.09375000	0.0059259390
LYVE1+MØ3	1957	1762.0	915.0	0.08952959	0.0069808030
Mast	1543	959.0	687.0	0.04864865	0.0018957346
Meso	718	2022.5	1082.0	0.13934426	0.0078040273
MØ_AgP	1278	4147.5	1505.0	0.13934426	0.0526469800
MØ_mod	1313	3057.0	1254.0	0.08952959	0.0545101840
Mo_pi	1652	1116.0	623.5	0.14712154	0.0657877400
nan	33998	2215.0	1250.5	0.14712154	0.0116054155
NC1	3129	863.0	616.0	0.07482993	0.0028328612
NC2	445	2545.0	1551.0	0.11926606	0.0029779630
NC3	147	2140.0	1189.0	0.12547529	0.0099825310
NC4	126	4615.5	2140.0	0.13934426	0.0017484877
NC5	90	2926.0	1721.5	0.04371099	0.0014946246
NC6	24	1321.0	911.0	0.16014437	0.0021411657
NK	3628	2022.5	980.5	0.06565657	0.0282486680
NKT	1463	1128.0	674.0	0.09375000	0.0327654000
NØ	121	1091.0	497.0	0.13214990	0.0732064400
PC1_vent	50095	1223.0	742.0	0.07482993	0.0026702270
PC2_atria	11323	1442.0	877.0	0.07161804	0.0017605633
PC3_str	14231	1706.0	998.0	0.11347518	0.0194489470
PC4_CMC-like	2207	2897.0	1551.0	0.15555556	0.0016936850
SMC1_basic	13114	1618.0	923.0	0.08177905	0.0031491345
SMC2_art	3128	3517.0	1501.0	0.10299003	0.0263081000
vCM1	71162	3593.0	1547.0	0.10299003	0.0006570302
vCM2	25658	4019.0	1734.0	0.10806175	0.0006337638
vCM3	20168	3514.0	1506.0	0.10806175	0.0006070417
vCM4	7651	4055.0	1657.0	0.11347518	0.0006937218
vCM5	650	3606.0	1683.5	0.16473318	0.0010016029

Expression

Embedding

# start
FEATURES_SELECTED_A <- c(
    "ENSG00000092054_MYH7",
    "ENSG00000111245_MYL2",
    "ENSG00000115641_FHL2",
    "ENSG00000175206_NPPA",
    "ENSG00000106631_MYL7",
    "ENSG00000198336_MYL4",
    "ENSG00000133392_MYH11",
    "ENSG00000149591_TAGLN",
    "ENSG00000107796_ACTA2"
)

FEATURES_SELECTED_B <- c(
    "ENSG00000143248_RGS5",
    "ENSG00000069431_ABCC9",
    "ENSG00000121361_KCNJ8",
    "ENSG00000119927_GPAM",
    "ENSG00000169710_FASN",
    "ENSG00000174697_LEP",
    "ENSG00000011465_DCN",
    "ENSG00000148180_GSN",
    "ENSG00000134853_PDGFRA"
)

FEATURES_SELECTED_C <- c(
    "ENSG00000110799_VWF",
    "ENSG00000261371_PECAM1",
    "ENSG00000179776_CDH5",
    "ENSG00000123560_PLP1",
    "ENSG00000179915_NRXN1",
    "ENSG00000021645_NRXN3",
    "ENSG00000170458_CD14",
    "ENSG00000173372_C1QA",
    "ENSG00000129226_CD68",
    "ENSG00000153563_CD8A",
    "ENSG00000168685_IL7R",
    "ENSG00000102245_CD40LG",
    "ENSG00000102854_MSLN",
    "ENSG00000184937_WT1",
    "ENSG00000169594_BNC1"
)

FEATURES_SELECTED <- c(
    FEATURES_SELECTED_A,
    FEATURES_SELECTED_B,
    FEATURES_SELECTED_C
)

Grey: cells included in the publication but have low UMI counts after re-mapping.

GEOM_POINT_SIZE <- 0.15

purrr::map(FEATURES_SELECTED, function(x) {
    SELECTED_FEATURE <- x

    plot_embedding(
        embedding = embedding_predefined[, c("x_umap", "y_umap")],
        color_values = log10(
            calc_cpm_anndata(
                anndata = adata,
                k = SELECTED_FEATURE,
                cells = embedding_predefined$cell
            ) + 1
        ),
        label = paste0(EMBEDDING_TITLE_PREFIX, "; ", SELECTED_FEATURE),
        label_position = NULL,
        show_color_value_labels = FALSE,
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        geom_point_alpha = 1,
        sort_values = TRUE,
        shuffle_values = FALSE,
        label_size = 2.5,
        label_hjust = 0,
        label_vjust = 0,
        rasterise = TRUE,
        dpi = 600,
        legend_size = 3,
        legend_ncol = 1
    ) +
        theme_customized(
            x = 0.035,
            y = 0.995,
            legend_key_size = 1.5,
            legend_text_size = 4,
            border_color = "#b3b3b3",
            strip_color = "#b3b3b3"
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Lollipop

vCM

FEATURES_SELECTED <- c(
    "ENSG00000115641_FHL2",
    "ENSG00000092054_MYH7",
    "ENSG00000111245_MYL2",
    "ENSG00000169851_PCDH7",
    "ENSG00000169760_NLGN1",
    #
    "ENSG00000140945_CDH13",
    "ENSG00000186314_PRELID2",
    "ENSG00000282917_AC107068.2",
    "ENSG00000249797_LINC02147",
    #
    "ENSG00000162614_NEXN",
    "ENSG00000148677_ANKRD1",
    "ENSG00000022267_FHL1",
    "ENSG00000163092_XIRP2",
    "ENSG00000100345_MYH9",
    "ENSG00000198842_DUSP27",
    "ENSG00000168334_XIRP1",
    "ENSG00000130176_CNN1",
    #
    "ENSG00000109846_CRYAB",
    "ENSG00000101608_MYL12A",
    "ENSG00000075624_ACTB",
    "ENSG00000189043_NDUFA4",
    "ENSG00000127184_COX7C",
    "ENSG00000135940_COX5B",
    "ENSG00000147123_NDUFB11",
    #
    "ENSG00000164741_DLC1",
    "ENSG00000164330_EBF1",
    "ENSG00000134532_SOX5",
    "ENSG00000221818_EBF2",
    "ENSG00000169282_KCNAB1"
)

cells_states_selected <- c(
    "vCM1", "vCM2", "vCM3", "vCM4", "vCM5"
)
cells_selected_lollipop <- purrr::map(cells_states_selected, function(x) {
    embedding_predefined %>%
        filter(cell_states == x) %>%
        pull(cell)
})
names(cells_selected_lollipop) <- cells_states_selected

matrix_cpm_subset <- purrr::map(FEATURES_SELECTED, function(x) {
    v <- calc_cpm_anndata(
        anndata = adata,
        k = x,
        cells = embedding_predefined$cell
    )
    v[is.na(v)] <- 0

    return(v)
}) %>%
    purrr::reduce(rbind)
rownames(matrix_cpm_subset) <- FEATURES_SELECTED

Extended Data Fig. 3a

plot_lollipop(
    cells = cells_selected_lollipop,
    features = FEATURES_SELECTED,
    matrix_cpm = matrix_cpm_subset,
    color_range_limits = NULL
)

aCM

# grep(pattern = "gsn", x = rownames(adata$var), ignore.case = TRUE, value = TRUE)
FEATURES_SELECTED <- c(
    "ENSG00000197616_MYH6",
    "ENSG00000175206_NPPA",
    "ENSG00000198336_MYL4",
    "ENSG00000181072_CHRM2",
    "ENSG00000117114_ADGRL2",
    #
    "ENSG00000115641_FHL2",
    "ENSG00000184347_SLIT3",
    "ENSG00000105697_HAMP",
    "ENSG00000128918_ALDH1A2",
    "ENSG00000204928_GRXCR2",
    "ENSG00000162670_BRINP3",
    #
    "ENSG00000138347_MYPN",
    "ENSG00000211455_STK38L",
    "ENSG00000154553_PDLIM3",
    "ENSG00000198842_DUSP27",
    "ENSG00000100345_MYH9",
    "ENSG00000130176_CNN1",
    #
    "ENSG00000104879_CKM",
    "ENSG00000189043_NDUFA4",
    "ENSG00000131143_COX4I1",
    "ENSG00000121769_FABP3",
    #
    "ENSG00000164741_DLC1",
    "ENSG00000127472_PLA2G5",
    "ENSG00000184384_MAML2",
    "ENSG00000249669_CARMN",
    "ENSG00000148180_GSN"
)

cells_states_selected <- c(
    "aCM1", "aCM2", "aCM3", "aCM4", "aCM5"
)
cells_selected_lollipop <- purrr::map(cells_states_selected, function(x) {
    embedding_predefined %>%
        filter(cell_states == x) %>%
        pull(cell)
})
names(cells_selected_lollipop) <- cells_states_selected

matrix_cpm_subset <- purrr::map(FEATURES_SELECTED, function(x) {
    v <- calc_cpm_anndata(
        anndata = adata,
        k = x,
        cells = embedding_predefined$cell
    )
    v[is.na(v)] <- 0

    return(v)
}) %>%
    purrr::reduce(rbind)
rownames(matrix_cpm_subset) <- FEATURES_SELECTED

Extended Data Fig. 3b

plot_lollipop(
    cells = cells_selected_lollipop,
    features = FEATURES_SELECTED,
    matrix_cpm = matrix_cpm_subset,
    color_range_limits = NULL
)

Celluar composition

prepare_cluster_composition(
    embedding = embedding_predefined,
    x = region,
    group = cell_type
) %>%
    plot_barplot(
        x = region,
        y = percentage,
        z = cell_type,
        legend_ncol = 2
    ) +
    theme_bw(base_size = 6) +
    theme(
        legend.key.size = grid::unit(3, "mm")
    )

R session info

devtools::session_info()$platform

##  setting  value                       
##  version  R version 4.0.3 (2020-10-10)
##  os       macOS  11.2                 
##  system   x86_64, darwin20.2.0        
##  ui       unknown                     
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2021-02-03

devtools::session_info()$pack %>%
    as_tibble() %>%
    dplyr::select(
        package,
        loadedversion,
        date,
        `source`
    ) %>%
    # print(n = nrow(.))
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

package	loadedversion	date	source
assertthat	0.2.1	2019-03-21	CRAN (R 4.0.0)
backports	1.2.1	2020-12-09	CRAN (R 4.0.3)
beeswarm	0.2.3	2016-04-25	CRAN (R 4.0.0)
broom	0.7.4.9000	2021-02-02	Github (tidymodels/broom@b750e4f)
bslib	0.2.4.9000	2021-02-02	Github (rstudio/bslib@b3cd7a9)
cachem	1.0.1	2021-01-22	Github (r-lib/cachem@27c8d89)
callr	3.5.1.9000	2021-01-06	Github (r-lib/callr@743069f)
cellranger	1.1.0	2016-07-27	CRAN (R 4.0.0)
checkmate	2.0.0	2020-02-06	CRAN (R 4.0.0)
cli	2.3.0	2021-01-31	CRAN (R 4.0.3)
colorspace	2.0-0	2020-11-10	R-Forge (R 4.0.3)
crayon	1.4.0	2021-01-30	CRAN (R 4.0.3)
DBI	1.1.1	2021-01-15	CRAN (R 4.0.3)
dbplyr	2.0.0	2020-11-03	CRAN (R 4.0.3)
desc	1.2.0	2018-05-01	CRAN (R 4.0.0)
devtools	2.3.1.9000	2021-01-21	Github (r-lib/devtools@ef962e4)
digest	0.6.27	2020-10-24	CRAN (R 4.0.3)
dplyr	1.0.4.9000	2021-02-02	Github (tidyverse/dplyr@2455c77)
ellipsis	0.3.1	2020-05-15	CRAN (R 4.0.3)
evaluate	0.14	2019-05-28	CRAN (R 4.0.0)
extrafont	0.17	2014-12-08	CRAN (R 4.0.2)
extrafontdb	1.0	2012-06-11	CRAN (R 4.0.0)
fansi	0.4.2	2021-01-15	CRAN (R 4.0.3)
farver	2.0.3	2020-01-16	CRAN (R 4.0.0)
fastmap	1.1.0	2021-01-25	CRAN (R 4.0.3)
forcats	0.5.1.9000	2021-01-28	Github (tidyverse/forcats@b5fce89)
fs	1.5.0	2020-07-31	CRAN (R 4.0.3)
generics	0.1.0	2020-10-31	CRAN (R 4.0.3)
ggbeeswarm	0.6.0	2017-08-07	CRAN (R 4.0.3)
ggplot2	3.3.3.9000	2021-01-30	Github (tidyverse/ggplot2@dbd7d79)
ggrastr	0.2.1	2021-01-27	Github (VPetukhov/ggrastr@f1ce011)
glue	1.4.1.9000	2021-01-06	Github (tidyverse/glue@f0a7b2a)
gt	0.2.2	2020-11-25	Github (rstudio/gt@bae32f4)
gtable	0.3.0	2019-03-25	CRAN (R 4.0.0)
haven	2.3.1	2020-06-01	CRAN (R 4.0.0)
highr	0.8	2019-03-20	CRAN (R 4.0.0)
hms	1.0.0	2021-01-13	CRAN (R 4.0.3)
htmltools	0.5.1.9000	2021-01-23	Github (rstudio/htmltools@e7f0393)
httr	1.4.2	2020-07-20	CRAN (R 4.0.2)
jquerylib	0.1.3	2020-12-17	Github (rstudio/jquerylib@8f8e639)
jsonlite	1.7.2	2020-12-09	CRAN (R 4.0.3)
knitr	1.31.4	2021-01-29	Github (yihui/knitr@d83e8de)
labeling	0.4.2	2020-10-20	CRAN (R 4.0.3)
lattice	0.20-41	2020-04-02	CRAN (R 4.0.3)
lifecycle	0.2.0	2020-03-06	CRAN (R 4.0.0)
lubridate	1.7.9.2	2021-01-04	Github (tidyverse/lubridate@aab2e30)
magrittr	2.0.1.9000	2020-12-14	Github (tidyverse/magrittr@bb1c86a)
Matrix	1.3-2	2021-01-06	CRAN (R 4.0.3)
memoise	2.0.0	2021-01-26	CRAN (R 4.0.3)
modelr	0.1.8.9000	2021-01-23	Github (tidyverse/modelr@16168e0)
munsell	0.5.0	2018-06-12	CRAN (R 4.0.0)
patchwork	1.1.1	2020-12-17	CRAN (R 4.0.3)
pillar	1.4.99.9006	2021-02-02	Github (r-lib/pillar@c6f8311)
pkgbuild	1.2.0	2020-12-15	CRAN (R 4.0.3)
pkgconfig	2.0.3	2019-09-22	CRAN (R 4.0.0)
pkgload	1.1.0	2020-05-29	CRAN (R 4.0.0)
png	0.1-7	2013-12-03	CRAN (R 4.0.0)
prettyunits	1.1.1.9000	2020-11-23	Github (r-lib/prettyunits@b1cdad8)
processx	3.4.5	2020-11-30	CRAN (R 4.0.3)
ps	1.5.0	2020-12-05	CRAN (R 4.0.3)
purrr	0.3.4.9000	2020-11-23	Github (tidyverse/purrr@af06d45)
R6	2.5.0	2020-11-02	Github (r-lib/R6@6cf7d4e)
ragg	1.0.0.9000	2021-01-15	Github (r-lib/ragg@aebed7f)
Rcpp	1.0.6	2021-01-15	CRAN (R 4.0.3)
readr	1.4.0.9000	2021-01-23	Github (tidyverse/readr@483ca6c)
readxl	1.3.1.9000	2021-01-23	Github (tidyverse/readxl@9f85fa5)
remotes	2.2.0.9000	2021-01-25	Github (r-lib/remotes@cf2b4a9)
reprex	1.0.0	2021-01-27	CRAN (R 4.0.3)
reticulate	1.18	2020-10-25	CRAN (R 4.0.3)
rlang	0.4.10.9000	2021-02-02	Github (r-lib/rlang@d15299e)
rmarkdown	2.6.6	2021-02-02	Github (rstudio/rmarkdown@d8e7a15)
rprojroot	2.0.2	2020-11-15	CRAN (R 4.0.3)
rstudioapi	0.13.0-9000	2021-01-02	Github (rstudio/rstudioapi@4baeb39)
Rttf2pt1	1.3.8	2020-01-10	CRAN (R 4.0.0)
rvest	0.3.6	2020-07-25	CRAN (R 4.0.2)
sass	0.3.1	2021-01-24	CRAN (R 4.0.3)
scales	1.1.1	2020-05-11	CRAN (R 4.0.3)
sessioninfo	1.1.1	2018-11-05	CRAN (R 4.0.3)
stringi	1.5.3	2020-09-09	CRAN (R 4.0.2)
stringr	1.4.0.9000	2021-01-23	Github (tidyverse/stringr@1f03eb0)
styler	1.3.2.9000	2021-02-02	Github (r-lib/styler@3560b39)
systemfonts	1.0.0.9000	2021-02-02	Github (r-lib/systemfonts@d85abe2)
testthat	3.0.1	2021-01-29	Github (r-lib/testthat@b19b5ac)
textshaping	0.2.1.9000	2021-01-15	Github (r-lib/textshaping@f6f2697)
tibble	3.0.6.9000	2021-01-31	Github (tidyverse/tibble@eb99cb6)
tidyr	1.1.2.9000	2021-01-23	Github (tidyverse/tidyr@c338aa9)
tidyselect	1.1.0	2020-05-11	CRAN (R 4.0.3)
tidyverse	1.3.0.9000	2020-11-23	Github (hadley/tidyverse@8a0bb99)
usethis	2.0.0.9000	2021-01-31	Github (r-lib/usethis@716b703)
utf8	1.1.4	2018-05-24	CRAN (R 4.0.0)
vctrs	0.3.6	2020-12-17	CRAN (R 4.0.3)
vipor	0.4.5	2017-03-22	CRAN (R 4.0.0)
viridisLite	0.3.0	2018-02-01	CRAN (R 4.0.0)
withr	2.4.1	2021-01-26	CRAN (R 4.0.3)
xfun	0.20	2021-01-06	CRAN (R 4.0.3)
xml2	1.3.2	2020-04-23	CRAN (R 4.0.0)
yaml	2.2.1	2020-02-01	CRAN (R 4.0.0)

Cells of the adult human heart

JD

2021-02-03